A technology of identifying individuals by face recognition is one of the important fields of study in biometrics. A principal problem which decreases the performance of individual identification is variations in facial appearance in an image. A main cause of the variations in appearance includes illumination, the posture, and degradation of the image. The degradation of the image includes low resolutions, blur, and noises. The blur causes two problems in the face recognition.
A first problem is a case in which a person is not identified correctly although he/she is an original person. The reason is that the facial image is not similar to the original person due to the variation of the image. In other words, it is a case in which FRR (False Rejection Rate) is increased.
A second problem is a case in which a person is identified erroneously as a different person. The reason is that the states of the facial images are similar among different persons. In other words, it is a case in which FAR (False Acceptance Rate) is increased.
In order to solve these problems, two methods shown below are proposed. In a first method, variation of the image is learned by blurring the image artificially. In a second method, the blur is removed by restoring the image for identification.
The first method is performed as follows. First of all, a target image is assumed not to be blurred. Then the target image is artificially blurred to increase target data. Accordingly, the FRR is restrained. However, there still remains the problem of high FAR because the target data is similar to other persons. Furthermore, there arises a problem of increase in storage capacity of the target data.
The second method is performed as follows. The image is restored to a state before being blurred by a method of restoring the image such as blind deconvolution and a super-resolution. The restored image is used for identifying the individual. The second method is applicable to a case in which the target image is blurred and hence is effective for identifying the individual. In the image restoration, the process of degradation due to the blur is represented by a point spread function. In order to obtain a performance of individual identification, it is important to estimate the point spread function from the degraded image with high degree of accuracy.
A method of using an edge width of a contour of an object is proposed in JP-A-2005-332382 (KOKAI) for estimating the point spread function. This method uses a phenomenon such that an edge of the image in focus is sharp and has a narrow width, and an edge of the blurred image is smooth and has a wide width. In JP-A-2005-33238 (KOKAI), a face area or pupil is detected from the blurred image, and then the contour thereof is detected. The edge width is obtained from the cross-section of the edge on the contour and is compared with a histogram of the edge width which is learned from the blurred image in advance.
However, in the method shown above, when the image is blurred, the contour is not clear, and hence the contour is difficult to detect in comparison with the detection of the face area or the pupil. When the result of detection of the contour includes error, the shape of the cross-section of the edge is significantly different.
Therefore, there is a problem such that the edge width obtained from the cross-section of the edge is not a stable feature for estimating the point spread function. In the case where the image includes noises, there also arises a problem that the edge width is an unstable feature.